Automatic generation of as-is BEM models of buildings

Oliver, Antonio Adán; Ramón, Amanda; Vivancos, José-Luis; Vilar, A. B.; Aparicio-Fernández, Carolina

doi:10.1016/j.jobe.2023.106865

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…They emphasized the use of explainable machine learning and advanced algorithms to support sustainable development goals. Adan et al [23] described a method for developing 3D semantic thermal models of buildings and converting them into standard BEM formats for energy simulations. This method streamlines the process of producing BEM files from thermal point clouds, making energy performance analyses even easier.…”

Section: Introductionmentioning

confidence: 99%

Configuration Optimization for Sustainable Temporary Houses Employing BIM Procedure

Tirella,

Fabbricatore,

Carpino

et al. 2023

Buildings

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Building information modeling (BIM) is a process for managing the construction information model. The possibility of managing very complex construction orders using information processes has made the work of designers both easier and more productive. In this research, a new process of “assisted design” was implemented for self-assembling temporary houses with high environmental sustainability. The developed procedure allows for the supporting of a nonexpert user in the configuration of housing modules, using the base elements, and assisting them until the energy verification process based on the place of installation. Furthermore, a lifecycle assessment of emissions is made available. This is the result of the interoperability of software enabled by using BIM logic. The proposed work contributes to enriching the range of technical solutions developed to meet the demand for temporary buildings, providing a valid support tool in the design phase, especially when it needs to be completed within a short timeframe. This study introduces a smart configuration procedure that combines aspects of project automation and flexibility with energy efficiency and environmental sustainability needs required for the green transition of building stock.

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Section: Introductionmentioning

confidence: 99%

Configuration Optimization for Sustainable Temporary Houses Employing BIM Procedure

Tirella,

Fabbricatore,

Carpino

et al. 2023

Buildings

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“…Volume calculations in the construction and demolition (C&D) phases can be automated from laser scanner, UAV, or satellite syn-thetic aperture radar (SAR) footage with the help of semantic segmentation [8,9]. Reverse engineering digital twins of existing buildings (as built and scan to BIM) requires assigning element classes to scan data to reconstruct element geometries [10,11]. Site progress monitoring can be automated through 3D component recognition and comparison with a planned database [12,13].…”

Section: Introductionmentioning

confidence: 99%

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

Rauch

Braml

2023

Applied Sciences

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Point cloud learning has recently gained strong attention due to its applications in various fields, like computer vision, robotics, and autonomous driving. Point cloud semantic segmentation (PCSS) enables the automatic extraction of semantic information from 3D point cloud data, which makes it a desirable task for construction-related applications as well. Yet, only a limited number of publications have applied deep-learning-based methods to address point cloud understanding for civil engineering problems, and there is still a lack of comprehensive reviews and evaluations of PCSS methods tailored to such use cases. This paper aims to address this gap by providing a survey of recent advances in deep-learning-based PCSS methods and relating them to the challenges of the construction industry. We introduce its significance for the industry and provide a comprehensive look-up table of publicly available datasets for point cloud understanding, with evaluations based on data scene type, sensors, and point features. We address the problem of class imbalance in 3D data for machine learning, provide a compendium of commonly used evaluation metrics for PCSS, and summarize the most significant deep learning methods developed for PCSS. Finally, we discuss the advantages and disadvantages of the methods for specific industry challenges. Our contribution, to the best of our knowledge, is the first survey paper that comprehensively covers deep-learning-based methods for semantic segmentation tasks tailored to construction applications. This paper serves as a useful reference for prospective research and practitioners seeking to develop more accurate and efficient PCSS methods.

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An experimental methodology for the calibration of indoor building environment models using thermal point clouds and CFD simulation

Ramón-Constantí,

Adán-Oliver,

Castilla-Pascual

et al. 2024

Advances in Building Energy Research

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Automatic generation of as-is BEM models of buildings

Cited by 3 publications

References 21 publications

Configuration Optimization for Sustainable Temporary Houses Employing BIM Procedure

Configuration Optimization for Sustainable Temporary Houses Employing BIM Procedure

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

An experimental methodology for the calibration of indoor building environment models using thermal point clouds and CFD simulation

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